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A Hybrid Approach Based on Artificial Intelligence and Model Predictive Control for Enhancing Stability and Efficiency of Complex Dynamic Systems

Journal of Robotics and Control (JRC) · Vol. 6 No. 5 (2025) · pp. 2426–2435

DOI
10.18196/jrc.v6i5.28069
ISSN
2715-5072, 2715-5056
Published
2025-10-19
Publisher
Universitas Muhammadiyah Yogyakarta
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Stability and operational performance in uncertain and time-varying nonlinear dynamic systems, such as robotic manipulators and autonomous vehicles, is an important problem in modern nonlinear dynamical systems. This study presents a hybrid model that integrates Long Short-Term Memory (LSTM)-based artificial intelligence (AI) to identify adaptive system changes with Model Predictive Control (MPC), provided with the help of an autoencoder that leads to dimensionality reduction. The proposed AI-MPC system offers conditional stability and real-time feasibility (guaranteed by a broad Lyapunov-based theoretical framework with a clear definition of AI in the stability guarantees). Among the contributions to research are scalable and computationally efficient control architecture that adaptively models complex system dynamics, minimizes control computation time, and remains robust to disturbances and uncertainties. It is also known that the implemented framework can be useful to a wide range of simulated and experimental case studies such as robotic arms and autonomous vehicle platforms, the framework has offered improved tracking performances with a root mean square error (RMSE) by more than 58 times compared to conventional MPC and PID controllers, uses less energy by up to 50 times less and disturbances with an order of magnitude (more than 20 times) reduced. The control iterations use 30-60 milliseconds that is why it is better to use it in real time. The extension of this AI-MPC system is that the control systems can be predictive in an unpredictable and nonlinear environment that may represent the interface to resource-constrained and safety-critical systems.

Keywords

Collaboration network of the authors

40 authors · 123 co-authorship links · 47 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Hillah
  • Baghdad College of Economic Sciences University
  • Prince Sattam Bin Abdulaziz University
  • Al-Mustaqbal University
  • University of Al-Ameed
  • University of Diyala
  • Al-Farahidi University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ravi SekharAffiliation unknown3
Amer IbrahimAffiliation unknown3
Ali Najim AbdullahUniversity of Diyala2
Ali Talib JawadUniversity of Hillah2
Dalael Saad Abdul-ZahraUniversity of Hillah2
Israa Al_BarazanchiAffiliation unknown2
Nouby M. GhazalyAffiliation unknown2
Husam Ali HadiAffiliation unknown2
Abdallah KassemAffiliation unknown2
Hassan AmoudAffiliation unknown2
Saadaldeen Rashid AhmedAffiliation unknown2
Karam S. KhalidAffiliation unknown2
Jamal Fadhil TawfeqAl-Farahidi University2
Poh Soon JosephNgINTI International University2
Hala Shaker MehdyMustansiriyah University1
Nariman Jabbar QasimAl-Esraa University1
Haider Hadi AbbasAl Mansour University College1
Aws Hamed HamadMinistry of Higher Education and Scientific Research1
Adnan Yousif DawodUniversity of Kirkuk1
Mohammed Fakhrulddin AbdulqaderBaghdad College of Economic Sciences University1
Yitong NiuAnyang University1
Ahmed Mohammed MerzaUniversity of Warith Al-Anbiyaa1
Suhad Ibraheem KadhemBaghdad College of Economic Sciences University1
Hissah AlmutairiPrince Sattam Bin Abdulaziz University1
Abdullah AlqahtaniPrince Sattam Bin Abdulaziz University1
Zinah S. JabbarImam Ja’afar Al-Sadiq University1
Mahmood Jamal AbdulhasanAffiliation unknown1
Priyanka Tupe-WaghmareSymbiosis International University1
Rafah Kareem MahmoodAffiliation unknown1
Ans Ibrahim MahameedAffiliation unknown1
Noor Q. LateefAffiliation unknown1
Hasanain M. JasimAffiliation unknown1
Ziyaad H. SalehAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Hybrid Approach Based on Artificial Intelligence and Model Predictive Control for Enhancing Stability and Efficiency of Complex Dynamic Systems</dc:title>
	<dc:creator>Nadweh, Safwan</dc:creator>
	<dc:creator>Al Sayed, Intisar A.M.</dc:creator>
	<dc:creator>Abdulbaqi, Azmi Shawkat</dc:creator>
	<dc:creator>Essa, Reyad Omran</dc:creator>
	<dc:creator>Sham, Rohana</dc:creator>
	<dc:creator>Gheni, Hassan Muwafaq</dc:creator>
	<dc:creator>Radhi, Ahmed Dheyaa</dc:creator>
	<dc:subject xml:lang="en-US">Dimensionality Reduction</dc:subject>
	<dc:subject xml:lang="en-US">Dynamic Systems</dc:subject>
	<dc:subject xml:lang="en-US">Hybrid AI-MPC Framework</dc:subject>
	<dc:subject xml:lang="en-US">LSTM Networks</dc:subject>
	<dc:subject xml:lang="en-US">Lyapunov Stability</dc:subject>
	<dc:subject xml:lang="en-US">Model Predictive Control (MPC)</dc:subject>
	<dc:subject xml:lang="en-US">Real-Time Control</dc:subject>
	<dc:subject xml:lang="en-US">Stability Enhancement</dc:subject>
	<dc:subject xml:lang="en-US">System Efficiency</dc:subject>
	<dc:description xml:lang="en-US">Stability and operational performance in uncertain and time-varying nonlinear dynamic systems, such as robotic manipulators and autonomous vehicles, is an important problem in modern nonlinear dynamical systems. This study presents a hybrid model that integrates Long Short-Term Memory (LSTM)-based artificial intelligence (AI) to identify adaptive system changes with Model Predictive Control (MPC), provided with the help of an autoencoder that leads to dimensionality reduction. The proposed AI-MPC system offers conditional stability and real-time feasibility (guaranteed by a broad Lyapunov-based theoretical framework with a clear definition of AI in the stability guarantees). Among the contributions to research are scalable and computationally efficient control architecture that adaptively models complex system dynamics, minimizes control computation time, and remains robust to disturbances and uncertainties. It is also known that the implemented framework can be useful to a wide range of simulated and experimental case studies such as robotic arms and autonomous vehicle platforms, the framework has offered improved tracking performances with a root mean square error (RMSE) by more than 58 times compared to conventional MPC and PID controllers, uses less energy by up to 50 times less and disturbances with an order of magnitude (more than 20 times) reduced. The control iterations use 30-60 milliseconds that is why it is better to use it in real time. The extension of this AI-MPC system is that the control systems can be predictive in an unpredictable and nonlinear environment that may represent the interface to resource-constrained and safety-critical systems.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-10-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/28069</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i5.28069</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 5 (2025); 2426-2435</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/28069/11951</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Safwan Nadweh, Intisar A.M. Al Sayed, Azmi Shawkat Abdulbaqi, Reyad Omran Essa, Rohana Sham, Hassan Muwafaq Gheni, Ahmed Dheyaa Radhi</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>